A method and computing device for inferring an airflow of a controlled appliance operating in an area of a building. The computing device stores a predictive model. The computing device determines a measured airflow of the controlled appliance and a plurality of consecutive temperature measurements in the area. The computing device executes a neural network inference engine using the predictive model for inferring an inferred airflow based on inputs. The inputs comprise the measured airflow and the plurality of consecutive temperature measurements. The inputs may further include at least one of a plurality of consecutive humidity level measurements in the area and a plurality of consecutive carbon dioxide (CO2) level measurements in the area. For instance, the controlled appliance is a Variable Air Volume (VAV) appliance and a K factor of the VAV appliance is calculated based on the inferred airflow.

A method includes obtaining a planning model for an industrial facility at a master MPC controller and sending at least one optimization call from the master MPC controller to one or more slave MPC controllers. The method also includes receiving at least one proxy limit value from the slave MPC controller(s) in response to the at least one optimization call. The at least one proxy limit value identifies to what extent one or more process variables controlled by the slave MPC controller(s) are adjustable without violating any process variable constraints. In addition, the method includes performing plantwide optimization at the master MPC controller using the planning model and the at least one proxy limit value. The at least one proxy limit value allows the master MPC controller to honor the process variable constraints of the slave MPC controller(s) during the plantwide optimization.

A control system for a gas turbine engine including a power turbine is disclosed. The control system may include a control module to receive engine operating goals and an estimated current engine state, wherein the estimated current engine state is produced by a model-based estimation module using a bandwidth signal produced by an adaptation logic module. The control module is operative to determine fuel flow, inlet guide vane schedules and stability bleed schedules based at least in part on the received engine operating goals and the estimated current engine state, and to send signals to a gas generator of the gas turbine engine in order to control the gas generator according to the determined fuel flow, inlet guide vane schedules and stability bleed schedules.

A method and device is provided that uses historical process data for an industrial process run by an industrial process control and automation system having processing equipment and identified by tag names. The tag names and process data are stored in a database, and a processor and a memory implement a Key Performance Indicator (KPI) tag system, that identifies primary and secondary KPIs from the list of tag names and data values stored in the database and extracts a set of KPI tags from the primary and secondary KPIs storing the extracted KPI tags in the memory. In another embodiment the KPI tag system imports all of the Human Machine Interface (HMI) display files used in the industrial process and identifies the display file tag names and links to other HMI display files. Using an asset model of the process plant and the links to the other HMI displays, the Level 1 HMI displays are identified and KPI tag system generates and stores the KPI tags for the identified Level 1 HMI displays.

A system and method for performing management and diagnostic functions in an advanced process control (APC) system. An APC management computer retrieves operating process data from an APC control computer and performs an iterative step test on the APC system. The iterative step test modifies at least one test parameter of the operating process data and identifies changes to a set of remaining parameters of the operating process data resulting from modification of the test parameter. The APC management computer determines at least one process variable from the iterative step test and generates at least one process model based on the process variable. The APC management computer transmits the process model to the APC control computer.

A method of developing a predictive model may include identifying mixing protocol parameters for the predictive model, identifying an evaluation criterion for the predictive model, selecting test values for the mixing protocol parameters, identifying a computational fluid dynamics (CFD) simulation required to be performed in order to generate the evaluation criteria, conducting the CFD simulation for each combination of test values, thereby generating evaluation criteria corresponding to each combination of test values, generating a domain of potential predictive models relating the mixing protocol parameters to the evaluation criterion, identifying a pool of candidate predictive models from the domain of potential predictive models, and ranking the pool of candidate predictive models.

For an easily implementable method for model predictive control of a DC/DC converter, and a corresponding controller, with which the optimization problem of the model predictive control can also be solved sufficiently quickly with large prediction horizons, the optimization problem is divided into two optimization problems by a model predictive output variable control and a model predictive choke current control being implemented in the control unit (10), wherein: the strands of the multiphase DC/DC converter (12) for the output variable control are combined into a single strand; a time-discrete state space model is produced therefrom; and the output variable control predicts the input voltage (u.sub.v,k+1) of the next sampling step (k+1) for this single strand on the basis of a first cost function (J.sub.v) of the optimization problem of the output variable control, said input voltage being given to the choke current control as a setpoint and the choke current control determining therefrom the necessary switch positions of the switches (S1, S2, S3, S4, S5, S6) of the strands of the multiphase DC/DC converter (12) for the next sampling step (k+1) on the basis of a second cost function (J.sub.i) of the optimization problem of the choke current control.

Alighting system may include: at least one controller which may be configured to: obtain lighting logging data including feature information related to features of the lighting system and obtained from a plurality of feature spaces; determine lighting prediction data which predicts at least one component failure in the lighting system at a future time in accordance with the lighting logging data and include at least one complex feature; model predicted component failures which are predicted to occur at a future time in accordance with the lighting prediction data and maintenance cost; and/or store the predicted component failures model in a memory.

Computer-based methods and systems provide automated batch data alignment for a batch production industrial process. An example embodiment selects a reference batch from batch data for a subject industrial process and configures batch alignment settings. In turn, a seed model configured to predict alignment quality given settings for one or more alignment hyperparameters is constructed. Collectively the selected reference batch, the configured batch alignment settings, the constructed seed model, and a set of representative batches, representative of the batch data for the industrial process, are used to perform at least one of: (i) automated active learning, (ii) interactive active learning, and (iii) guided learning to determine settings for the one or more alignment hyperparameters. Then, a batch alignment is performed using the determined settings for the one or more alignment hyperparameters and the configured batch alignment settings. The resulting aligned batch data of the subject industrial process enables improved modeling and control of batch productions by the subject industrial process.

The subject matter herein provides for AI-based prediction of production defects in association with a production system, such as a semiconductor manufacturing machine. In one embodiment, a method begins by receiving production data from the production system. The production data typically comprises non-homogeneous machine parameters and maintenance data, quality test data, and product and process data. Using the production data, a neural network is trained to model an operation of a given machine in the production system. Preferably, the training involves multi-task learning, transfer learning (e.g., using knowledge obtained with respect to a machine of the same type as the given machine), and a combination of multi-task learning and transfer learning. Once the model is trained, it is associated with the given machine operating environment, wherein it is used to provide quality assurance predictions.